Data for: Genomic signals of local adaptation across climatically heterogenous habitats in an invasive tropical fruit fly (Bactrocera tryoni)
Data files
Sep 21, 2023 version files 32.71 MB
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BayPass_input.geno
928.58 KB
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BayPass_nativevsAliceSprings.ecotype
56 B
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BayPass_nativevsExpanded.ecotype
65 B
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BayPass_nativevsPacificIslands.ecotype
57 B
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BayPass_nativevsPooled.ecotype
68 B
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bialSNP_MAF05_geno_LDdecayInput.vcf
11.61 MB
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bioclim_minus2.txt
1.69 KB
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conStruct_coordinates_latlon.txt
4.34 KB
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conStruct_distances.txt
1.49 MB
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conStruct_input
5.15 MB
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EEMS_params_chain1.ini
290 B
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EEMS.coord
3.92 KB
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EEMS.diffs
1.53 MB
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EEMS.outer
240 B
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FinalFiltered_301samples.vcf
8.55 MB
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pop_alleleFreq_minus2.txt
1.42 MB
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population_map_301samples.txt
5.76 KB
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README.md
3.14 KB
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sNMF_input.geno
2.03 MB
Abstract
Local adaptation plays a key role in the successful establishment of pest populations in new environments by enabling them to tolerate novel biotic and abiotic conditions experienced outside their native range. However, the genomic underpinnings of such adaptive responses remain unclear, especially for agriculturally important pests. We investigated population genomic signatures in the tropical/subtropical Queensland fruit fly, Bactrocera tryoni, which has an expanded range encompassing temperate and arid zones in Australia, and tropical zones in the Pacific Islands. Using reduced representation sequencing data from 28 populations, we detected allele frequency shifts associated with the native/invasive status of populations and identified environmental factors that have likely driven population differentiation. We also determined that precipitation, temperature, and geographic variables explain allelic shifts across the distribution range of B. tryoni. We found spatial heterogeneity in signatures of local adaptation across various climatic conditions in invaded areas. Specifically, disjunct invasive populations in the tropical Pacific Islands and arid zones of Australia were characterised by multiple significantly differentiated single nucleotide polymorphisms (SNPs), some of which were associated with genes with well-understood function in environmental stress (e.g., heat and desiccation) response. However, invasive populations in southeast Australian temperate zones showed higher gene flow with the native range and lacked a strong local adaptive signal. These results suggest that population connectivity with the native range has differentially affected local adaptive patterns in different invasive populations. Overall, our findings provide insights into the evolutionary underpinnings of invasion success of an important horticultural pest in climatically distinct environments.
https://doi.org/10.5061/dryad.kkwh70s9q
Description of the data and file structure
1. bioclim_minus2.txt: Six uncorrelated bioclimatic variables (BIO3: Isothermality; BIO5: Max Temperature of Warmest Month - deg C; BIO8: Mean Temperature of Wettest Quarter - deg C; BIO9: Mean Temperature of Driest Quarter - deg C; BIO12: Annual Precipitation - mm; BIO19: Precipitation of Coldest Quarter - mm) for all populations excluding Mareeba and Weipa used as input file for the GF analysis.
2. BayPass_input.geno: BayPass input file in geno format for all 28 populations.
3. BayPass_nativevsAliceSprings.ecotype: BayPass ecotype file for contrasting native populations vs. Alice Springs.
4. BayPass_nativevsExpanded.ecotype: BayPass ecotype file for contrasting native populations vs. southern expanded populations.
5. BayPass_nativevsPacificIslands.ecotype: BayPass ecotype file for contrasting native populations vs. Tahiti and Loyalty islands.
6. BayPass_nativevsPooled.ecotype: BayPass ecotype file for contrasting native populations vs. all invasive populations.
7. bialSNP_MAF05_geno_LDdecayInput.vcf: VCF containing SNPs filtered for biallelic markers with minimum allele frequency (MAF) of 0.05 and missing data but not for linkage disequilibrium (LD). This VCF was used for LD decay analysis.
8. conStruct_coordinates_latlon.txt: Coordinates of 301 samples in latitude and longitude format used in the conStruct analysis.
9. conStruct_distances.txt: Matrix of pairwise geographic distances between each individual used in the conStruct analysis.
10. conStruct_input: Matrix of allele frequencies created using the structure2conStruct function of the R package conStruct.
11. EEMS.coord: Coordinates of 301 samples used in EEMS analysis.
12. EEMS.diffs: Genetic dissimilarity matrix created using the bed2diffs_v1 R function, used as the input file for EEMS analysis.
13. EEMS.outer: Enclosed habitat polygon created using the Google Maps API v3 tool, used for EEMS analysis.
14. EEMS_params_chain1.ini: Parameters for the EEMS analysis.
15. FinalFiltered_301samples.vcf: Final VCF containing SNPs filtered for biallelic markers, MAF, missing data, and LD.
16. pop_alleleFreq_minus2.txt: Population allele frequencies (excluding Mareeba and Weipa) used as input file for the GF analysis.
17. population_map_301samples.txt: Population information for samples.
18. sNMF_input.geno: sNMF input file.
Sharing/Access information
No new genomic data was generated as part of this study.
Raw DartSeq data was derived from the following source:
- Popa-Báez Á-D, Catullo R, Lee SF, Yeap HL, Mourant RG, Frommer M, et al. (2020). Genome-wide patterns of differentiation over space and time in the Queensland fruit fly. Sci Rep 10: 10788.
Code/Software
Related scripts are available at GitHub: https://github.com/Elahep/B.tryoni\_PopGenomics